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Three-level main-effects designs exploiting prior information about model uncertainty
Authors:Pi-Wen Tsai  Steven G. Gilmour  Roger Mead
Affiliation:1. Division of Biostatistics and Bioinformatics, National Health Research Institutes, Zhunan Campus, 350 Taiwan, ROC;2. School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London E1 4NS, UK;3. School of Applied Statistics, The University of Reading, PO Box 240, Earley Gate, Reading RG6 6FN, UK
Abstract:
To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs.
Keywords:Bayesian optimal designs   Factor screening   Orthogonal array   Prior information   Projection
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